Mitigating the Top 10 Agentic AI Threats

As AI evolves from passive large language models (LLMs) into autonomous, decision-making agents, the threat landscape is shifting dramatically. These agentic systems—capable of planning, reasoning, and acting independently—introduce new risks that traditional governance frameworks are ill-equipped to handle. Enterprises must now anticipate and mitigate threats that arise not just from data misuse or model bias, but from the very autonomy and adaptability of these agents. This blog outlines ten critical threat vectors associated with agentic AI and offers actionable governance strategies to defend against them. It is designed for enterprise leaders in risk, privacy, security, and legal functions who are shaping the future of responsible AI deployment.

Top 10 threats and how to defend them

1. Memory Poisoning

Exploiting an AI’s memory systems – both short- and long-term – to introduce malicious or false data and corrupt the agent’s context, leading to altered decision-making or unauthorized operations.

Defense – implement memory content validation session isolation, robust authentication mechanisms for memory access, anomaly detection systems, and regular memory sanitization routines.

2. Tool Misuse

When attackers manipulate AI agents to abuse their integrated tools through deceptive prompts or commands, operating within authorized permissions; might include agent hijacking where the AI agent ingests manipulated data and then executes unintended actions that trigger malicious tool interactions.

Defense – enforce strict tool access verification, monitor tool usage patterns, set operational boundaries, and validate agent instructions.

3. Privilege Compromise

Arises when attackers exploit weaknesses in permission management to perform unauthorized actions, often involving dynamic role inheritance or misconfigurations.

Defense – implement granular permission controls, dynamic access validation, robust monitoring of role changes, and thoro9ugh auditing of privilege operations.

4. Resource Overload

Targets the computational, memory, and service capacities of AI systems to degrade performance or cause failures.

Defense – deploy resource management controls, implement adaptive scaling mechanisms, establish quotas, and monitor system load in real-time; also implement AI rate-limiting policies to restrict high-frequency task requests per agent session.

5. Cascading Hallucination Attacks

Exploiting an AI’s tendency to generate contextually plausible but false information, propagating through systems and disrupting decision-making.

Defense – establish robust output validation mechanisms, implement behavioral constraints, deploy multi-source validation, and ensure ongoing system corrections through feedback loops; also require secondary validation before AI-generated knowledge can be used in decision-making processes.

6. Intent Breaking and Goal Manipulation

Exploits vulnerabilities in an AI agent’s planning and goal-setting capabilities, allowing attackers to manipulate or redirect the agent’s objectives and reasoning.

Defense – implement planning validation frameworks, boundary management for reflection processes, and dynamic protection mechanisms for goal alignment, then deploy AI behavioral auditing to flag significant goal deviations.

7. Overwhelming Human Overseers

Targets systems monitored by humans for decision validation, aiming to exploit human cognitive limitations or compromise interaction frameworks.

Defense – develop advanced human-AI interaction frameworks and adaptive trust mechanisms, adjusting the level of human oversight based on risk, confidence, and context.

8. Agent Communication Poisoning

When attackers manipulate communication channels between AI agents to spread false information, disrupt workflows, or negatively influence decision-making.

Defense – deploy cryptographic message authentication, enforce communication validation policies, and monitor inter-agent interactions for anomalies while also requiring multi-agent consensus verification for mission-critical decision-making processes.

9. Rogue Agents in Multi-Agent System

Involves malicious or compromised AI agents operating outside normal monitoring boundaries, executing unauthorized actions or exfiltrating data.

Defense – restrict AI agent autonomy using policy constraints and continuous behavioral monitoring; maintain integrity via controlled hosting environments, regular AI red teaming, and input/output monitoring for deviations.

10. Privacy

Extensive access to user data -- including internet activity, personal applications (like emails and calendars), and third-party systems (such as financial accounts) - heightens the risk of unauthorized data exposure, particularly if the agent’s systems are compromised.

Defense – establish clear data usage policies and robust consent mechanisms, ensuring users are well-informed about the data being accessed; also, prioritize transparency regarding how AI agents make decisions and implement mechanisms for user intervention in case of errors, helping balance the convenience offered by AI agents with privacy concerns and enhancing accountability for the agents' actions in complex environments.

This is by no means an exhaustive list. There are more considerations that can put your system at risk. AI is a powerful tool that is transforming business across all sizes, industry, geographic, and regulatory boundaries. Making sure you implement AI effectively takes expertise and caution.

Wipro can help

If you need help with your AI governance program, contact us. Our consultants are prepared to work with you in establishing an AI regimen that works for you.

About the Authors

Ivana Bartoletti

Global Chief Privacy and AI Governance Officer, Wipro. A leading voice in responsible AI, Ivana advises governments and enterprises on privacy, ethics, and regulation.

Anindito De
Practice Head, AI Technology Services, Wipro. Anindito leads innovation in agentic systems, orchestration frameworks, and enterprise AI deployment.

Josey George V
Director, AI Strategy & Risk, Wipro. Josey specializes in AI risk modeling, governance frameworks, and regulatory alignment.